VeridisQuo: Open Source Deepfake Detector

VeridisQuo is an open source deepfake detection system that integrates spatial and frequency analysis with explainable AI techniques. The system utilizes EfficientNet-B4 for spatial feature extraction and combines it with frequency analysis using DCT 8×8 blocks and FFT radial bins, resulting in a 2816-dimensional feature vector that feeds into an MLP classifier. This approach not only enhances the accuracy of deepfake detection but also provides insights into the decision-making process through techniques like GradCAM, making the model’s predictions more interpretable. Understanding and detecting deepfakes is crucial in maintaining the integrity of digital media and combating misinformation.

The development of VeridisQuo, an open-source deepfake detection system, is a significant advancement in the ongoing battle against misinformation and digital deception. By combining spatial and frequency analysis with explainable AI, this system offers a sophisticated approach to identifying deepfakes, which are increasingly used to spread false information. The use of EfficientNet-B4 for spatial analysis and DCT/FFT for frequency analysis allows for a comprehensive examination of video content, making it more difficult for deepfakes to evade detection. This matters because as deepfake technology becomes more accessible, the potential for misuse grows, posing threats to privacy, security, and trust in digital media.

EfficientNet-B4, a convolutional neural network, is utilized to extract spatial features from video frames, providing a 1792-dimensional feature representation. This network is known for its efficiency and accuracy, making it well-suited for the task of deepfake detection. The spatial analysis focuses on the visual elements of the video, such as textures and patterns, which can be indicative of manipulation. By capturing these details, the system can effectively differentiate between authentic and altered content, thus enhancing the reliability of the detection process.

In addition to spatial analysis, VeridisQuo incorporates frequency analysis using Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT). These techniques analyze the frequency components of video frames, which can reveal subtle artifacts introduced during the deepfake creation process. The fusion of DCT and FFT results in a 1024-dimensional feature set that complements the spatial features, leading to a more robust detection system. This dual approach ensures that both visual and frequency-based anomalies are considered, increasing the likelihood of identifying deepfakes with high accuracy.

Moreover, the integration of explainable AI through GradCAM provides transparency in the detection process, allowing users to understand which parts of the video contributed to the deepfake classification. This explainability is crucial for building trust in AI systems, as it enables users to verify and interpret the results. In an era where digital content can be easily manipulated, having a tool like VeridisQuo not only empowers individuals and organizations to protect themselves against deepfake threats but also promotes accountability and transparency in the use of AI technologies.

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